from IPython.display import Audio, display, HTML
import matplotlib.pyplot as plt
import numpy as np
import os
%load_ext autoreload
%autoreload 1
%aimport wavenet_project
w = wavenet_project
The autoreload extension is already loaded. To reload it, use: %reload_ext autoreload
RATE = 44100
def hide_axes(axes):
for ax in axes:
ax.axis('off')
for i, filename in enumerate(os.listdir('pred')):
fig, axes = plt.subplots(3, dpi=100)
mel_path = os.path.join('wavenet_vocoder/egs/mol/dump/lj/logmelspectrogram/norm/eval/',
filename)
truth_path = os.path.join('wavenet_vocoder/egs/mol/dump/lj/logmelspectrogram/norm/eval/',
filename.replace('feats', 'wave'))
pred_path = os.path.join('pred', filename)
mel = np.load(mel_path)
truth_wave = np.load(truth_path)
pred_wave = np.load(pred_path)
axes[0].set_title('MusicNet example #%d mel spectrogram' % (i+1))
axes[0].imshow(mel, aspect='auto', cmap='coolwarm', interpolation='nearest', origin='lower')
axes[1].set_title('MusicNet example #%d waveform' % (i+1))
axes[1].plot(truth_wave)
axes[2].set_title('Waveform predicted by WaveNet')
axes[2].plot(pred_wave)
# hide_axes(axes)
plt.tight_layout()
plt.show()
display(HTML('<p>MusicNet example #%d</p>' % (i+1)))
display(Audio(truth_wave, rate=RATE))
display(HTML('<p>WaveNet prediction</p>'))
display(Audio(pred_wave, rate=RATE))
MusicNet example #1
WaveNet prediction
MusicNet example #2
WaveNet prediction
MusicNet example #3
WaveNet prediction